Ioannis Paraskevas

Combination of magnitude and phase statistical features for audio classification

The increasing demand for the retrieval and classification of audio utterances from multimedia databases, gives rise to the need for the implementation of effective feature extraction techniques. Most recent techniques employ temporal-related features and magnitude spectral features. In the proposed method, we use both the magnitude and phase spectrum of the signals to derive the features. By overcoming the discontinuity problems of phase, phase may be used as an additional feature stream. The experimental results derived from ten classes of gunshots show that, for certain classes, there is an improvement of 14% when both magnitude and phase information is employed, compared to the case when only the magnitude feature vector is used. Also, the results reported here show that the reliability of the method is increased, demonstrating the complementary nature of magnitude and phase.

Natural soundscapes and identification of environmental sounds: A pattern recognition approach

“Soundscapes” are maps that depict the sound content of an area at a time interval. Sound features encapsulate information which can be combined with the visual features of a landscape, in order to produce useful ecological observations/data, for areas of environmental or ecological interest. These include monitoring of the wildlife, the inhabitation and the use/human activities of the area, as they evolve with time. In this paper, a method is proposed for the development of a soundscape - a procedure that requires a hierarchical, coarser-to-finer classification scheme for the environmental sounds. The proposed method is illustrated for echolocation calls produced by different species of bats. Time-frequency representations of the sound signals are obtained as a basis for feature extraction. Vectors of statistical features are classified by an Artificial Neural Network classifier. The experimental results verify the potential of the proposed method for classification of environmental sounds within a soundscape development task.

Audio fine classification using the statistical analysis of acoustic images

The fine classification of audio utterances is an important problem because the features that have to be extracted need to be very accurate in order to contribute to effective classification. In this paper, results are presented for a fine classification problem: namely the classification of two groups of different kinds of gunshots. The problem of accurate classification can be divided into two parts: (i) feature extraction and (ii) classification. The more effective the feature extraction, the more effectively the classifier will be able to categorize the various audio samples. In this paper, a novel method for the automatic recognition of acoustic utterances is presented using acoustic images as the basis for the feature extraction. The feature extraction process is based on the time‐frequency distribution of an acoustic unit. A novel feature extraction technique based on the statistical analysis of the spectrogram Hartley transform (distribution) and Choi–Williams distributions of the data is reported...